Our client is building the agentic AI layer for oncology EHRs — replacing manual clinical abstraction with task-driven AI agents that process pathology reports, clinical notes, genomic panels, and imaging reports in real time.

Trusted by top US cancer centers. 10x growth in the last year. Millions of oncology documents processed monthly. Backed by Battery Ventures, Lightspeed, and General Catalyst.

1. Own end-to-end ML workstreams — from problem framing to production delivery
2. Set technical direction: architectures, evaluation protocols, and release criteria independently
3. Build LLM/NLP systems for clinical extraction, trial matching, RWE abstraction, and structured output generation
4. Lead experiment design, ablation planning, and model iteration cycles
5. Review designs, code, and experiment plans from Research Engineers
6. Mentor Senior and Research Engineers; raise team execution quality
7. Define and enforce engineering standards — reproducibility, versioning, documentation
8. Write technical design docs and represent ML workstreams cross-functionally
9. Proactively identify and resolve risks, technical debt, and quality gaps

Must Have

1. 5–10 years in ML/NLP/LLM engineering with a strong production track record
2. Proven ownership of technical workstreams — not just contributions
3. Expert Python; production-grade ML code across the full stack
4. Deep expertise in LLMs, RAG, fine-tuning (LoRA/SFT/DPO), prompt engineering, and structured outputs
5. Strong evaluation thinking — metrics design, hidden test sets, failure mode analysis
6. Experience mentoring engineers and running design reviews
7. Disciplined engineering habits — versioning, testing, reproducibility, peer review

Good to Have

1. Clinical NLP, biomedical NLP, EHR data, oncology, RWE, or cancer registry experience
2. Familiarity with vLLM, Ray, LangChain/LlamaIndex, MLflow/W&B, or vector databases
3. Background in weak supervision, active learning, RLHF, or synthetic data generation
4. Prior tech lead, staff engineer, or workstream owner experience in healthcare/regulated AI

1. Direct ownership of workstreams that impact cancer patient outcomes at scale
2. Hard, meaningful problems at the intersection of AI, clinical data, and healthcare
3. Best-in-industry compensation for senior technical talent
4. Clear growth path to Functional Lead / Associate Director within 12 months
5. Comprehensive health insurance for you and your family
6. Flexible, output-driven working hours
7. Daily office lunch + Zomato meal benefits